Learning routing queries in a query zone
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Some Formal Analysis of Rocchio's Similarity-Based Relevance Feedback Algorithm
Information Retrieval
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Tracking multiple topics for finding interesting articles
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
What Happened to Content-Based Information Filtering?
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Immune Learning in a Dynamic Information Environment
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Measuring the interestingness of articles in a limited user environment
Information Processing and Management: an International Journal
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We show that users have different reading behavior when evaluating the interestingness of articles, calling for different parameter configurations for information retrieval algorithms for different users. Better recommendation results can be made if parameters for common information retrieval algorithms, such as the Rocchio algorithm, are learned dynamically instead of being statically fixed a priori. By dynamically learning good parameter configurations, Rocchio can adapt to differences in user behavior among users. We show that by adaptively learning online the parameters of a simple retrieval algorithm, similar recommendation performance can be achieved as more complex algorithms or algorithms that require extensive fine-tuning. Also we have also shon that online parameter learning can yield 10% better results than best performing filter from the TREC11 adaptive filter task.